Battery temperature prediction method, electronic device, storage medium and program product

Abstract

The invention provides a battery temperature prediction method, electronic equipment, a storage medium and a program product, and relates to the technical field of battery management. The method comprises the following steps: acquiring battery thermal management data of a vehicle battery in real time; according to the battery thermal management data, parameter values of a battery temperature mechanism model corresponding to the vehicle battery are identified online, and the battery temperature mechanism model is used for describing the heat production and heat dissipation process of the vehicle battery; correspondingly adjusting parameters of the battery temperature mechanism model according to the parameter values; and according to the adjusted parameters and the battery thermal management data, the temperature of the vehicle battery after the set duration is predicted. According to the invention, the prediction precision of the vehicle battery temperature is improved.

Description

Technical Field

[0001] This application relates to the field of battery management technology, and in particular to a battery temperature prediction method, electronic device, storage medium, and program product. Background Technology

[0002] Lithium-ion batteries are a critical component in electric vehicles, portable electronic devices, and energy storage systems, making temperature management during operation crucial. Battery temperature directly impacts safety, performance, cycle life, and charging efficiency. Especially under high-rate charge / discharge conditions, the internal heat generation of lithium-ion batteries increases dramatically. If the temperature exceeds the safety threshold, it will not only accelerate battery aging and reduce capacity but may even trigger safety incidents such as thermal runaway. Conversely, maintaining the battery temperature within a suitable range helps improve charging efficiency, shorten charging time, and effectively prevent harmful side reactions such as lithium plating. Therefore, there is an urgent need for an accurate and real-time method to predict battery temperature.

[0003] Currently, battery temperature prediction methods are mainly based on battery temperature mechanism models. These models, such as electrochemical-thermal coupling models, predict temperature by establishing physical equations relating the internal electrochemical reactions to heat generation and transfer. However, the parameters of these models typically require a series of experimental data to determine. This model-based approach often results in lower accuracy in battery temperature predictions under certain operating conditions. Summary of the Invention

[0004] This application provides a battery temperature prediction method, electronic equipment, storage medium, and program products to improve the accuracy of vehicle battery temperature prediction.

[0005] In a first aspect, this application provides a battery temperature prediction method, including:

[0006] Real-time acquisition of vehicle battery thermal management data;

[0007] Based on battery thermal management data, the parameter values ​​of the battery temperature mechanism model corresponding to the vehicle battery are identified online. The battery temperature mechanism model is used to describe the heat generation and heat dissipation process of the vehicle battery.

[0008] Adjust the parameters of the battery temperature mechanism model accordingly based on the parameter values;

[0009] Based on the adjusted parameters and battery thermal management data, the temperature of the vehicle battery after a set period of time is predicted.

[0010] In one possible implementation, based on battery thermal management data, the parameter values ​​of the battery temperature mechanism model corresponding to the vehicle battery are identified online, including:

[0011] Based on the battery thermal management data and the parameters of the battery temperature mechanism model at the previous moment, determine the prediction estimation error of the battery temperature mechanism model;

[0012] Based on battery thermal management data, determine the gain value of the battery temperature mechanism model;

[0013] Based on the prediction estimation error and gain value, the parameter values ​​of the battery temperature mechanism model are identified online.

[0014] In one possible implementation, determining the gain value of the battery temperature mechanism model based on battery thermal management data includes:

[0015] Based on the battery thermal management data and the covariance matrix of the battery temperature mechanism model at the previous time step, the gain value of the battery temperature mechanism model is determined.

[0016] In one possible implementation, after determining the gain value of the battery temperature mechanism model, the method further includes:

[0017] The covariance matrix of the battery temperature mechanism model is obtained based on the covariance matrix of the battery temperature mechanism model at the previous time step and the gain value of the battery temperature mechanism model.

[0018] In one possible implementation, predicting the temperature of the vehicle battery after a set period of time based on adjusted parameters and battery thermal management data includes:

[0019] If the predicted value of the battery thermal management data after a set time can be obtained based on the battery thermal management data, then the battery thermal management data is discretely accumulated and summed.

[0020] Multiply the cumulative sum by the adjusted parameters to obtain the first multiplication result;

[0021] Add the result of the first multiplication to the temperature of the vehicle battery to obtain the temperature of the vehicle battery after a set time.

[0022] In one possible implementation, predicting the temperature of the vehicle battery after a set period of time based on adjusted parameters and battery thermal management data further includes:

[0023] If the predicted value of the battery thermal management data after a set time cannot be obtained based on the battery thermal management data, then the battery thermal management data is multiplied by the adjusted parameters to obtain the second multiplication result.

[0024] Multiply the second multiplication result by the set duration to obtain the third multiplication result;

[0025] Add the result of the third multiplication to the temperature of the vehicle battery to obtain the temperature of the vehicle battery after a set time.

[0026] Secondly, this application provides a battery temperature prediction device, comprising:

[0027] The acquisition module is used to acquire real-time battery thermal management data of the vehicle battery;

[0028] The identification module is used to identify the parameter values ​​of the battery temperature mechanism model corresponding to the vehicle battery online based on the battery thermal management data. The battery temperature mechanism model is used to describe the heat generation and heat dissipation process of the vehicle battery.

[0029] The adjustment module is used to adjust the parameters of the battery temperature mechanism model according to the parameter values.

[0030] The prediction module is used to predict the temperature of the vehicle battery after a set period of time, based on the adjusted parameters and battery thermal management data.

[0031] In one possible implementation, the identification module is specifically used for:

[0032] Based on the battery thermal management data and the parameters of the battery temperature mechanism model at the previous moment, determine the prediction estimation error of the battery temperature mechanism model;

[0033] Based on battery thermal management data, determine the gain value of the battery temperature mechanism model;

[0034] Based on the prediction estimation error and gain value, the parameter values ​​of the battery temperature mechanism model are identified online.

[0035] In one possible implementation, the identification module is specifically used for:

[0036] Based on the battery thermal management data and the covariance matrix of the battery temperature mechanism model at the previous time step, the gain value of the battery temperature mechanism model is determined.

[0037] In one possible implementation, the battery temperature prediction device further includes an obtaining module, which is specifically used for:

[0038] The covariance matrix of the battery temperature mechanism model is obtained based on the covariance matrix of the battery temperature mechanism model at the previous time step and the gain value of the battery temperature mechanism model.

[0039] In one possible implementation, the prediction module is specifically used for:

[0040] If the predicted value of the battery thermal management data after a set time can be obtained based on the battery thermal management data, then the battery thermal management data is discretely accumulated and summed.

[0041] Multiply the cumulative sum by the adjusted parameters to obtain the first multiplication result;

[0042] Add the result of the first multiplication to the temperature of the vehicle battery to obtain the temperature of the vehicle battery after a set time.

[0043] In one possible implementation, the prediction module is specifically used for:

[0044] If the predicted value of the battery thermal management data after a set time cannot be obtained based on the battery thermal management data, then the battery thermal management data is multiplied by the adjusted parameters to obtain the second multiplication result.

[0045] Multiply the second multiplication result by the set duration to obtain the third multiplication result;

[0046] Add the result of the third multiplication to the temperature of the vehicle battery to obtain the temperature of the vehicle battery after a set time.

[0047] Thirdly, this application provides an electronic device, including: a memory and a processor;

[0048] The memory stores instructions that the computer executes;

[0049] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0050] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed, are used to implement the first aspect and / or various possible embodiments of the first aspect.

[0051] Fifthly, this application provides a computer program product, including a computer program that, when executed, implements the first aspect and / or various possible implementations of the first aspect.

[0052] This application provides a battery temperature prediction method, electronic device, storage medium, and program product, relating to the field of battery management technology. The method includes: acquiring real-time battery thermal management data of a vehicle battery; identifying online parameter values ​​of a battery temperature mechanism model corresponding to the vehicle battery based on the battery thermal management data, wherein the battery temperature mechanism model describes the heat generation and dissipation processes of the vehicle battery; adjusting the parameters of the battery temperature mechanism model accordingly based on the parameter values; and predicting the temperature of the vehicle battery after a set time based on the adjusted parameters and the battery thermal management data. This application addresses the problem of non-adaptive parameters in existing battery temperature prediction methods by acquiring real-time battery thermal management data of the vehicle battery and identifying online parameter values ​​of the corresponding battery temperature mechanism model based on the acquired data, thereby improving the accuracy of vehicle battery temperature prediction. Attached Figure Description

[0053] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0054] Figure 1 A flowchart illustrating the battery temperature prediction method provided in this application embodiment. Figure 1 ;

[0055] Figure 2 A flowchart illustrating the battery temperature prediction method provided in this application embodiment. Figure 2 ;

[0056] Figure 3 This is a schematic diagram of the vehicle battery temperature prediction results provided in an embodiment of this application;

[0057] Figure 4 A schematic diagram showing the temperature prediction error results of the battery temperature prediction method provided in the embodiments of this application;

[0058] Figure 5 This is a schematic diagram of the battery temperature prediction device provided in the embodiments of this application;

[0059] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0060] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0061] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0062] Currently, methods for predicting battery temperature mainly include mechanistic model-based prediction and data-driven prediction methods. Data-driven prediction methods require massive amounts of historical data to train the model, and their internal mechanisms and logic are difficult to interpret, thus posing challenges to vehicle safety certification. Mechanism-based methods mostly identify model parameters offline through a series of experiments, which makes the parameters lack adaptability and may result in significant temperature prediction errors under conditions not covered by the experiments.

[0063] To address the aforementioned issues, this application provides a battery temperature prediction method, which involves acquiring real-time battery thermal management data of a vehicle battery; identifying the parameter values ​​of the corresponding battery temperature mechanism model online based on the battery thermal management data; adjusting the parameters of the battery temperature mechanism model accordingly based on the parameter values; and predicting the temperature of the vehicle battery after a set time based on the adjusted parameters and the battery thermal management data.

[0064] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0065] Figure 1 A flowchart illustrating the battery temperature prediction method provided in this application embodiment. Figure 1 ,like Figure 1 As shown, the method includes:

[0066] S101. Real-time acquisition of vehicle battery thermal management data.

[0067] In this step, it can be understood that when predicting the temperature of the vehicle battery, it is necessary to first acquire the battery thermal management data of the vehicle battery in real time. This battery thermal management data includes at least one of the following: heat exchange rate with air, heat generation rate of the vehicle battery charging and discharging current, coolant volumetric flow rate, coolant outlet temperature, coolant inlet temperature, and ambient temperature. It should be noted that this is merely an example, and the embodiments of this application are not intended to limit the scope of the invention.

[0068] S102. Based on battery thermal management data, identify the parameter values ​​of the battery temperature mechanism model corresponding to the vehicle battery online, wherein the battery temperature mechanism model is used to describe the heat generation and heat dissipation process of the vehicle battery.

[0069] After obtaining the battery thermal management data through S101, it is necessary to identify the parameter values ​​of the battery temperature mechanism model corresponding to the vehicle battery online based on the battery thermal management data.

[0070] For example, the battery temperature mechanism model can be constructed in the following way: based on the law of conservation of energy, an initial battery temperature mechanism model corresponding to the vehicle battery is established according to the battery thermal management data; the initial battery temperature mechanism model is discretized to obtain the battery temperature mechanism model.

[0071] Furthermore, the initial battery temperature mechanism model needs to be solved simultaneously by solving the following formulas:

[0072] (1);

[0073] (2);

[0074] (3);

[0075] (4).

[0076] Where, m b For vehicle battery quality; C b For the specific heat capacity of the vehicle battery; Q curr The heat generation rate is the charge / discharge current; Q thm For thermal management of coolant thermal efficiency; Q amb The heat exchange rate with air is denoted as T; the vehicle battery temperature is denoted as T; the vehicle battery charging and discharging current is denoted as I, where the charging current is negative; the vehicle battery open-circuit voltage is denoted as OCV; and the vehicle battery terminal voltage is denoted as U. p V represents the specific heat capacity of the coolant. cool This refers to the volumetric flow rate of the coolant. T is the density of the coolant. outlet T represents the coolant outlet temperature. inlet is the coolant inlet temperature; h is the heat transfer coefficient with air; A is the contact area between the vehicle battery and the air; T amb The ambient temperature.

[0077] Combining equations (1) to (4), we can obtain the initial battery temperature mechanism model:

[0078] (5).

[0079] After determining the initial battery temperature mechanism model, it is discretized to obtain the final battery temperature mechanism model. Specifically, the battery temperature mechanism model can be expressed by the following formula:

[0080] (6).

[0081] make , , Where Y represents the temperature rise of the vehicle battery from time 0 to t, and k = 1, 2, 3… Furthermore, the battery temperature mechanism model can also be expressed by the following formula: , This represents the numerical values ​​corresponding to the parameters of the battery temperature mechanism model.

[0082] X can be obtained by real-time collection of vehicle battery charging and discharging current, vehicle battery terminal voltage, coolant volumetric flow rate, coolant outlet temperature, and ambient temperature, and then calculated and summed. Y can be obtained by real-time collection of vehicle battery temperature and calculation of the temperature rise. Furthermore, based on X and Y data, an online fitting can be performed to obtain... .

[0083] S103. Adjust the parameters of the battery temperature mechanism model according to the parameter values.

[0084] In this step, it can be understood that the adjustment of the battery temperature mechanism model parameters is based on the parameter values ​​determined in S102, which enables the determined parameters to be adaptive. This adaptability means that the parameters are no longer static, preset fixed values, but can be dynamically and in real time adjusted according to the vehicle battery's thermal management data.

[0085] Furthermore, this adaptive feature can solve the problems of low efficiency and low prediction accuracy caused by the need for offline identification and determination of battery temperature mechanism model parameters based on a series of experimental data in existing battery temperature prediction methods.

[0086] S104. Based on the adjusted parameters and battery thermal management data, predict the temperature of the vehicle battery after a set time.

[0087] The specific implementation method for predicting the vehicle battery temperature after a set period of time based on the adjusted parameters and battery thermal management data can be selected according to the actual situation.

[0088] For example, predicting the temperature of a vehicle battery after a set period of time is based on whether the predicted value of the battery thermal management data after the set period of time can be obtained from the battery thermal management data.

[0089] In one implementation, predicting the temperature of a vehicle battery after a set period of time based on adjusted parameters and battery thermal management data includes: if a predicted value of the battery thermal management data after a set period of time can be obtained based on the battery thermal management data, then performing discrete cumulative summation on the battery thermal management data; multiplying the cumulative summation result by the adjusted parameters to obtain a first multiplication result; and adding the first multiplication result to the temperature of the vehicle battery to obtain the temperature of the vehicle battery after the set period of time.

[0090] In this implementation, the temperature of the vehicle battery after a set period of time can be expressed by the following formula:

[0091] (7).

[0092] In another implementation, predicting the vehicle battery temperature after a set time based on the adjusted parameters and battery thermal management data further includes: if the predicted value of the battery thermal management data after the set time cannot be obtained based on the battery thermal management data, then multiplying the battery thermal management data with the adjusted parameters to obtain a second multiplication result; multiplying the second multiplication result with the set time to obtain a third multiplication result; and adding the third multiplication result to the vehicle battery temperature to obtain the vehicle battery temperature after the set time.

[0093] In this implementation, the temperature of the vehicle battery after a set period of time can be expressed by the following formula:

[0094] (8).

[0095] It should be noted that the embodiments of this application do not impose any restrictions on the specific implementation method for determining the predicted value of battery thermal management data after a set time.

[0096] In summary, predicting the temperature of a vehicle battery after a set time period, and whether the predicted value of the battery thermal management data after a set time period can be obtained based on the battery thermal management data, can improve the accuracy of predicting the temperature of the vehicle battery after a set time period.

[0097] This application embodiment acquires real-time battery thermal management data of the vehicle battery and identifies the parameter values ​​of the corresponding battery temperature mechanism model online based on the acquired real-time battery thermal management data. The parameters of the battery temperature mechanism model are then adjusted online to the corresponding parameter values, thereby solving the problem that the parameters of the battery temperature mechanism model in existing battery temperature prediction methods lack adaptability. Based on the adjusted parameters and the real-time acquired battery thermal management data, the system predicts the problems of the vehicle battery after a set period of time, thereby improving the prediction accuracy of the vehicle battery temperature.

[0098] Based on the above embodiments, in some embodiments, S102 describes identifying the parameter values ​​of the battery temperature mechanism model corresponding to the vehicle battery online based on the battery thermal management data, including: determining the prediction estimation error of the battery temperature mechanism model based on the battery thermal management data and the parameters of the battery temperature mechanism model at the previous moment; determining the gain value of the battery temperature mechanism model based on the battery thermal management data; and identifying the parameter values ​​of the battery temperature mechanism model online based on the prediction estimation error and the gain value.

[0099] In the above embodiments, it can be understood that in order to identify the parameter values ​​of the battery temperature mechanism model corresponding to the vehicle battery online, it is necessary to determine the prediction estimation error of the battery temperature mechanism model based on the real-time acquired battery thermal management data and the parameters of the battery temperature mechanism model at the previous moment. Specifically, the prediction estimation error can be expressed in the following ways:

[0100] (9).

[0101] Optionally, the gain value of the battery temperature mechanism model is determined based on battery thermal management data, including: determining the gain value of the battery temperature mechanism model based on the battery thermal management data and the covariance matrix of the battery temperature mechanism model at the previous time step. Specifically, the gain value of the battery temperature mechanism model can be expressed by the following formula:

[0102] (10).

[0103] Where k(n) represents the gain value of the battery temperature mechanism model at the current moment; P(n-1) represents the covariance matrix of the battery temperature mechanism model at the previous moment; X(n) represents the forgetting factor; X(n) represents the battery thermal management data. This represents the parameter values ​​corresponding to the parameters of the battery temperature mechanism model at the previous moment.

[0104] Based on the aforementioned determined prediction estimation error and gain values, the parameter values ​​of the battery temperature mechanism model are identified online; that is, the adjustment values ​​of the battery temperature mechanism model parameters are determined based on the prediction estimation error and gain values. Furthermore, the parameter values ​​can be determined using the following formula:

[0105] (11).

[0106] in, This represents the parameter value corresponding to the parameters of the battery temperature mechanism model at the current moment.

[0107] In summary, the embodiments of this application utilize matrix operations to achieve online identification of parameter values ​​of the battery temperature mechanism model based on battery thermal management data, thereby avoiding black-box models.

[0108] Furthermore, in some examples, after determining the gain value of the battery temperature mechanism model, the process further includes: obtaining the covariance matrix of the battery temperature mechanism model based on the covariance matrix of the model at the previous time step and the gain value of the model. In these examples, it can be understood that after determining the gain value of the battery temperature mechanism model, the covariance matrix needs to be updated to update the gain value of the model at the next time step. Specifically, the covariance matrix of the battery temperature mechanism model can be expressed by the following formula:

[0109] (12).

[0110] In summary, the embodiments of this application utilize real-time collected battery thermal management data to iteratively update the gain value, prediction estimation error, and covariance matrix of the battery temperature mechanism model, enabling the prediction of battery temperature without the need for historical and experimental data, thus saving time and manpower costs to a certain extent.

[0111] The following will illustrate how to use the battery temperature prediction method provided in the embodiments of this application. Figure 2 A flowchart illustrating the battery temperature prediction method provided in this application embodiment. Figure 2 .like Figure 2 As shown, the method includes the following steps:

[0112] 1. Real-time acquisition of vehicle battery thermal management data. Battery thermal management data includes: vehicle battery charging and discharging current, vehicle battery terminal voltage, coolant volumetric flow rate, coolant outlet temperature, coolant inlet temperature, ambient temperature, and vehicle battery temperature.

[0113] 2. Based on battery thermal management data and the law of conservation of energy, establish an initial battery temperature mechanism model for the vehicle battery.

[0114] 3. Discretize the initial battery temperature mechanism model to obtain the battery temperature mechanism model.

[0115] 4. Based on battery thermal management data, identify the parameter values ​​of the battery temperature mechanism model corresponding to the vehicle battery online.

[0116] 5. Adjust the parameters of the battery temperature mechanism model according to the parameter values.

[0117] 6. Based on the future Can the vehicle battery temperature be predicted based on the vehicle battery charging and discharging current, coolant volumetric flow rate, coolant outlet temperature, coolant inlet temperature, and ambient temperature? Determine a method for predicting vehicle battery temperature, and based on the determined method, predict vehicle battery temperature using adjusted parameters and battery thermal management data.

[0118] Furthermore, based on battery thermal management data, the parameter values ​​of the battery temperature mechanism model corresponding to the vehicle battery can be identified online. This requires first initializing the parameters of the battery temperature mechanism model. Covariance matrix and forgetting factor Next, based on the battery thermal management data, the parameters of the battery temperature mechanism model are iteratively updated. The specific update principle has been explained in the previous embodiments and will not be repeated here.

[0119] It should be noted that the principle of identifying the parameter values ​​of the battery temperature mechanism model corresponding to the vehicle battery online is similar to that of the Recursive Least Squares (RLS) method. In other words, it can be considered that the embodiments of this application utilize the recursive least squares method to identify the parameter values ​​of the battery temperature mechanism model corresponding to the vehicle battery online based on battery thermal management data.

[0120] Furthermore, using DC charging process Taking 20 seconds as an example, let... =[0.0005;-0.0001;-0.0005;], =[ 0.01,0,0;0,0.01,0;0,0,0.01], =0.98, the vehicle battery temperature prediction results are as follows: Figure 3 . Figure 3 This is a schematic diagram of the vehicle battery temperature prediction results provided in an embodiment of this application.

[0121] To verify the prediction accuracy of the battery temperature prediction method provided in this application embodiment, this application embodiment compares the predicted battery temperature with the actual battery temperature. Figure 4 This is a schematic diagram of the temperature prediction error result of the battery temperature prediction method provided in the embodiments of this application.

[0122] In summary, this application provides a method for online prediction of battery temperature. By modeling the heat generation and dissipation behavior of a vehicle battery, identifying model parameters online, and predicting the vehicle battery temperature after a period of time based on the identified parameters, this method facilitates subsequent dynamic adjustment of the charging current and the on / off thresholds of the thermal management system, ensuring that the vehicle battery temperature remains within or near its optimal range.

[0123] Furthermore, compared with existing battery temperature prediction methods, the method provided in this application embodiment can identify battery temperature mechanism model parameters online and continuously update the parameters iteratively, resulting in better anti-aging properties of the battery temperature mechanism model. This application embodiment does not require historical data and experimental data, which can save time and manpower costs, while making it more adaptable to different battery cells. The algorithm used in the battery temperature prediction method provided in this application embodiment is relatively simple and easy to implement in embedded systems.

[0124] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0125] Figure 5 This is a schematic diagram of the battery temperature prediction device provided in the embodiments of this application, as shown below. Figure 5 As shown, the battery temperature prediction device 500 provided in this embodiment includes:

[0126] The acquisition module 501 is used to acquire battery thermal management data of the vehicle battery in real time.

[0127] The identification module 502 is used to identify the parameter values ​​of the battery temperature mechanism model corresponding to the vehicle battery online based on the battery thermal management data. The battery temperature mechanism model is used to describe the heat generation and heat dissipation process of the vehicle battery.

[0128] The adjustment module 503 is used to adjust the parameters of the battery temperature mechanism model according to the parameter values.

[0129] The prediction module 504 is used to predict the temperature of the vehicle battery after a set period of time based on the adjusted parameters and battery thermal management data.

[0130] In one possible implementation, the identification module 502 is specifically used for:

[0131] Based on the battery thermal management data and the parameters of the battery temperature mechanism model at the previous moment, determine the prediction estimation error of the battery temperature mechanism model;

[0132] Based on battery thermal management data, determine the gain value of the battery temperature mechanism model;

[0133] Based on the prediction estimation error and gain value, the parameter values ​​of the battery temperature mechanism model are identified online.

[0134] In one possible implementation, the identification module 502 is specifically used for:

[0135] Based on the battery thermal management data and the covariance matrix of the battery temperature mechanism model at the previous time step, the gain value of the battery temperature mechanism model is determined.

[0136] In one possible implementation, the battery temperature prediction device further includes an obtaining module, which is specifically used for:

[0137] The covariance matrix of the battery temperature mechanism model is obtained based on the covariance matrix of the battery temperature mechanism model at the previous time step and the gain value of the battery temperature mechanism model.

[0138] In one possible implementation, the prediction module 504 is specifically used for:

[0139] If the predicted value of the battery thermal management data after a set time can be obtained based on the battery thermal management data, then the battery thermal management data is discretely accumulated and summed.

[0140] Multiply the cumulative sum by the adjusted parameters to obtain the first multiplication result;

[0141] Add the result of the first multiplication to the temperature of the vehicle battery to obtain the temperature of the vehicle battery after a set time.

[0142] In one possible implementation, the prediction module 504 is specifically used for:

[0143] If the predicted value of the battery thermal management data after a set time cannot be obtained based on the battery thermal management data, then the battery thermal management data is multiplied by the adjusted parameters to obtain the second multiplication result.

[0144] Multiply the second multiplication result by the set duration to obtain the third multiplication result;

[0145] Add the result of the third multiplication to the temperature of the vehicle battery to obtain the temperature of the vehicle battery after a set time.

[0146] The battery temperature prediction device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0147] It should be noted that the division of the various modules in the above device is merely a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, these modules can be implemented entirely in software via processing element calls; they can be fully implemented in hardware; or some modules can be implemented by processing element calls to software, while others are implemented in hardware. For example, a processing module can be a separate processing element, or it can be integrated into a chip within the device. Alternatively, it can be stored as program code in the device's memory, and its functions can be called and executed by a processing element. The implementation of other modules is similar. Moreover, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element here can be an integrated circuit with signal processing capabilities. During implementation, each step of the above method or each of the above modules can be completed through integrated logic circuits in the hardware of the processor element or through software instructions.

[0148] For example, these modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs). As another example, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together as a System-On-a-Chip (SOC).

[0149] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 6 As shown, the electronic device 600 provided in this application embodiment may include: a processor 601, and a memory 602 communicatively connected to the processor, wherein:

[0150] The memory stores instructions that the computer executes;

[0151] The processor executes computer execution instructions stored in memory to implement the method described in the foregoing method embodiments.

[0152] It should be understood that processor 601 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the application can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor. Memory 602 may include high-speed random access memory (RAM), and may also include non-volatile memory (NVM), such as at least one disk storage device, or a USB flash drive, external hard drive, read-only memory, disk, or optical disc, etc.

[0153] Optionally, the electronic device 600 may also include a communication interface 603. In specific implementations, if the communication interface 603, memory 602, and processor 601 are implemented independently, they can be interconnected via a bus to complete communication. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc., but this does not imply that there is only one bus or one type of bus.

[0154] Optionally, in a specific implementation, if the communication interface 603, memory 602, and processor 601 are integrated on a single chip, then the communication interface 603, memory 602, and processor 601 can communicate through an internal interface.

[0155] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed, are used to implement the methods described in any of the foregoing embodiments.

[0156] It is understood that the computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0157] An exemplary computer-readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the computer-readable storage medium. Of course, the computer-readable storage medium can also be a component of the processor. The processor and the computer-readable storage medium can reside in an ASIC. Alternatively, the processor and the computer-readable storage medium can exist as discrete components in an electronic device.

[0158] The integrated modules implemented as software functional modules described above can be stored in a computer-readable storage medium. These software functional modules, stored in a computer-readable storage medium, include several instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of this application.

[0159] This application also provides a computer program product, including a computer program that, when executed, implements the method described in any of the foregoing embodiments.

[0160] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0161] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0162] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0163] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0164] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for predicting battery temperature, characterized in that, include: Real-time acquisition of vehicle battery thermal management data; Based on the battery thermal management data, the parameter values ​​of the battery temperature mechanism model corresponding to the vehicle battery are identified online, wherein the battery temperature mechanism model is used to describe the heat generation and heat dissipation process of the vehicle battery; Based on the parameter values, the parameters of the battery temperature mechanism model are adjusted accordingly; Based on the adjusted parameters and the battery thermal management data, the temperature of the vehicle battery after a set period of time is predicted.

2. The method according to claim 1, characterized in that, The step of identifying the parameter values ​​of the battery temperature mechanism model corresponding to the vehicle battery online based on the battery thermal management data includes: Based on the battery thermal management data and the parameters of the battery temperature mechanism model at the previous moment, determine the prediction estimation error of the battery temperature mechanism model; Based on the battery thermal management data, determine the gain value of the battery temperature mechanism model; Based on the prediction estimation error and the gain value, the parameter values ​​of the battery temperature mechanism model are identified online.

3. The method according to claim 2, characterized in that, The step of determining the gain value of the battery temperature mechanism model based on the battery thermal management data includes: Based on the battery thermal management data and the covariance matrix of the battery temperature mechanism model at the previous time step, the gain value of the battery temperature mechanism model is determined.

4. The method according to claim 3, characterized in that, After determining the gain value of the battery temperature mechanism model, the method further includes: The covariance matrix of the battery temperature mechanism model is obtained based on the covariance matrix of the battery temperature mechanism model at the previous time step and the gain value of the battery temperature mechanism model.

5. The method according to any one of claims 1 to 4, characterized in that, The step of predicting the temperature of the vehicle battery after a set period of time based on the adjusted parameters and the battery thermal management data includes: If the predicted value of the battery thermal management data after the set time can be obtained based on the battery thermal management data, then the battery thermal management data is discretely accumulated and summed. The result of the cumulative summation is multiplied by the adjusted parameter to obtain the first multiplication result; The first multiplication result is added to the temperature of the vehicle battery to obtain the temperature of the vehicle battery after the set time.

6. The method according to any one of claims 1 to 4, characterized in that, The step of predicting the temperature of the vehicle battery after a set period of time based on the adjusted parameters and the battery thermal management data further includes: If the predicted value of the battery thermal management data after the set time period cannot be obtained based on the battery thermal management data, then the battery thermal management data is multiplied by the adjusted parameter to obtain a second multiplication result; Multiply the second multiplication result by the set duration to obtain the third multiplication result; The third multiplication result is added to the temperature of the vehicle battery to obtain the temperature of the vehicle battery after the set time.

7. A battery temperature prediction device, characterized in that, include: The acquisition module is used to acquire real-time battery thermal management data of the vehicle battery; The identification module is used to identify the parameter values ​​of the battery temperature mechanism model corresponding to the vehicle battery online based on the battery thermal management data, wherein the battery temperature mechanism model is used to describe the heat generation and heat dissipation process of the vehicle battery. The adjustment module is used to adjust the parameters of the battery temperature mechanism model according to the parameter values. The prediction module is used to predict the temperature of the vehicle battery after a set period of time based on the adjusted parameters and the battery thermal management data.

8. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed, are used to implement the method as described in any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program that, when executed, implements the method described in any one of claims 1-6.

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